A fast algorithm for hierarchical text classification

Text classification is becoming more important with the proliferation of the Internet and the huge amount of data it transfers. We present an efficient algorithm for text classification using hierarchical classifiers based on a concept hierarchy. The simple TFIDF classifier is chosen to train sample data and to classify other new data. Despite its simplicity, results of experiments on Web pages and TV closed captions demonstrate high classification accuracy. Application of feature subset selection techniques improves the performance. Our algorithm is computationally efficient being bounded by O(n log n) for n samples.